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Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review

Author

Listed:
  • Yuan Luo

    (Northwestern University Feinberg School of Medicine)

  • William K. Thompson

    (Northwestern University Feinberg School of Medicine)

  • Timothy M. Herr

    (Northwestern University Feinberg School of Medicine)

  • Zexian Zeng

    (Northwestern University Feinberg School of Medicine)

  • Mark A. Berendsen

    (Galter Health Sciences Library, Northwestern University Feinberg School of Medicine)

  • Siddhartha R. Jonnalagadda

    (Northwestern University Feinberg School of Medicine
    Knowledge and Conversation Group, Microsoft)

  • Matthew B. Carson

    (Northwestern University Feinberg School of Medicine)

  • Justin Starren

    (Northwestern University Feinberg School of Medicine)

Abstract

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.

Suggested Citation

  • Yuan Luo & William K. Thompson & Timothy M. Herr & Zexian Zeng & Mark A. Berendsen & Siddhartha R. Jonnalagadda & Matthew B. Carson & Justin Starren, 2017. "Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review," Drug Safety, Springer, vol. 40(11), pages 1075-1089, November.
  • Handle: RePEc:spr:drugsa:v:40:y:2017:i:11:d:10.1007_s40264-017-0558-6
    DOI: 10.1007/s40264-017-0558-6
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    References listed on IDEAS

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    1. Juan M. Banda & Alison Callahan & Rainer Winnenburg & Howard R. Strasberg & Aurel Cami & Ben Y. Reis & Santiago Vilar & George Hripcsak & Michel Dumontier & Nigam Haresh Shah, 2016. "Feasibility of Prioritizing Drug–Drug-Event Associations Found in Electronic Health Records," Drug Safety, Springer, vol. 39(1), pages 45-57, January.
    2. Rave Harpaz & Alison Callahan & Suzanne Tamang & Yen Low & David Odgers & Sam Finlayson & Kenneth Jung & Paea LePendu & Nigam Shah, 2014. "Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art," Drug Safety, Springer, vol. 37(10), pages 777-790, October.
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    Cited by:

    1. Gianluca Trifirò & Janet Sultana & Andrew Bate, 2018. "From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources," Drug Safety, Springer, vol. 41(2), pages 143-149, February.
    2. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.
    3. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    4. Yiqing Zhao & Yue Yu & Hanyin Wang & Yikuan Li & Yu Deng & Guoqian Jiang & Yuan Luo, 2022. "Machine Learning in Causal Inference: Application in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 459-476, May.
    5. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    6. Tavpritesh Sethi & Nigam H. Shah, 2017. "Pharmacovigilance Using Textual Data: The Need to Go Deeper and Wider into the Con(text)," Drug Safety, Springer, vol. 40(11), pages 1047-1048, November.
    7. Na Zhang & Ping Yu & Yupeng Li & Wei Gao, 2022. "Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics," Sustainability, MDPI, vol. 14(24), pages 1-26, December.

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